Data Science Fellow - AI/NLP

About this role

(ID: 2026-2316)

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).

Benefits We Offer:

• 100% Medical, Dental & Vision Coverage for Employees

• Paid Time Off and Paid Holidays

• 401K match up to 5%

• Educational Benefits for Career Growth

• Employee Referral Bonus

• Flexible Spending Accounts:

• Healthcare (FSA)

• Parking Reimbursement Account (PRK)

• Dependent Care Assistant Program (DCAP)

• Transportation Reimbursement Account (TRN)

We are seeking a postdoctoral researcher to develop AI/NLP and knowledge engineering methods that transform biomedical literature, experimental protocols, and source evidence into structured, quarriable, and evidence-grounded knowledge for organoid protocol standardization and optimization.

The postdoc will work at the intersection of large language models, biomedical NLP, scientific document understanding, knowledge graphs, ontology grounding, computational biology, and human-in-the-loop curation. Potential projects include LLM-based protocol extraction, retrieval-augmented literature mining, curated knowledge graph construction, ontology and entity normalization, protocol comparison, consensus protocol derivation, benchmark design, and natural-language interfaces over structured biological knowledge.

Responsibilities

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Design and implement AI/NLP methods for biomedical literature mining and structured protocol knowledge extraction.

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Develop benchmark datasets, annotation guidelines, and evaluation pipelines for scientific information extraction.

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Build and evaluate RAG, in-context learning, fine-tuning, graph matching, entity normalization, and KG query workflows.

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Analyze extraction errors, model behavior, retrieval failures, grounding quality, and biological ambiguity.

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Collaborate with software engineers to integrate research methods into usable tools and reproducible pipelines.